Glucose sensor signal reliability analysis

ABSTRACT

Disclosed are methods, apparatuses, etc. for glucose sensor signal reliability analysis.

This application claims the benefit of priority to U.S. ProvisionalPatent Application No. 61/407,884, titled “Glucose Sensor ReliabilityAnalysis,” filed on Oct. 28, 2010, and assigned to the assignee ofclaimed subject matter.

BACKGROUND

1. Field

Subject matter disclosed herein relates to glucose sensor signalreliability analysis.

2. Information

The pancreas of a normal healthy person produces and releases insulininto the blood stream in response to elevated blood plasma glucoselevels. Beta cells (β-cells), which reside in the pancreas, produce andsecrete insulin into the blood stream as it is needed. If β-cells becomeincapacitated or die, which is a condition known as Type I diabetesmellitus (or in some cases, if (β-cells produce insufficient quantitiesof insulin, a condition known as Type II diabetes), then insulin may beprovided to a body from another source to maintain life or health.

Traditionally, because insulin cannot be taken orally, insulin has beeninjected with a syringe. More recently, the use of infusion pump therapyhas been increasing in a number of medical situations, including fordelivering insulin to diabetic individuals. For example, externalinfusion pumps may be worn on a belt, in a pocket, or the like, and theycan deliver insulin into a body via an infusion tube with a percutaneousneedle or a cannula placed in subcutaneous tissue.

As of 1995, less than 5% of the Type I diabetic individuals in theUnited States were using infusion pump therapy. Over time, greater than7% of the more than 900,000 Type I diabetic individuals in the U.S.began using infusion pump therapy. The percentage of Type I diabeticindividuals that use an infusion pump is now growing at a rate of over2% each year. Moreover, the number of Type II diabetic individuals isgrowing at 3% or more per year, and increasing numbers of insulin-usingType II diabetic individuals are also adopting infusion pumps.Physicians have recognized that continuous infusion can provide greatercontrol of a diabetic individual's condition, so they are increasinglyprescribing it for patients.

A closed-loop infusion pump system may include an infusion pump that isautomatically and/or semi-automatically controlled to infuse insulininto a patient. The infusion of insulin may be controlled to occur attimes and/or in amounts that are based, for example, on blood glucosemeasurements obtained from an embedded blood-glucose sensor, e.g., inreal-time. Closed-loop infusion pump systems may also employ thedelivery of glucagon, in addition to the delivery of insulin, forcontrolling blood-glucose and/or insulin levels of a patient (e.g., in ahypoglycemic context). Glucagon delivery may also be based, for example,on blood glucose measurements that are obtained from an embeddedblood-glucose sensor, e.g., in real-time.

SUMMARY

Briefly, example embodiments may relate to methods, systems,apparatuses, and/or articles, for analyzing the reliability of a glucosesensor signal. In one particular implementation, a method comprises:obtaining a plurality of glucose sensor measurements at a glucose sensorover a time interval; and detecting a change in responsiveness of saidglucose sensor to a presence of glucose in a fluid based, at least inpart, on an application of one or more thresholds to one or moresensitivity metrics, at least one of said sensitivity metrics beingbased, at least in part, on said glucose sensor measurements. In oneexample, the change in responsiveness comprises a decrease insensitivity of the glucose sensor to the presence of glucose in saidfluid. In another example, at least one of the sensitivity metricscomprises a measurement of dispersion of a rate change in said bloodglucose sensor measurements over at least a portion of the timeinterval. For example, the measurement of dispersion of said rate ofchange may comprise a variance of the rate of change. In another exampleimplementation, at least one of the sensitivity metrics comprises acomputed mean value of said sensor measurements obtained over saidportion of said time interval. In yet another example implementation, analert signal is generated in response to the detected change in saidresponsiveness. In yet another example implementation, the portion ofthe time interval comprises a sliding time window. In yet anotherembodiment, the one or more sensitivity metrics comprise at least ameasurement of dispersion of a rate change in the blood glucose sensormeasurements and a mean value of the sensor measurements over at least aportion of the time interval, and wherein a decrease in sensitivity ofthe sensor is detected if the measurement of dispersion of a rate changein said blood glucose sensor measurements does not exceed a firstthreshold and the mean value of said sensor measurements does not exceeda second threshold. In another implementation, the fluid comprisesinterstitial fluid. In yet another implementation, detecting the changein responsiveness of the glucose sensor to the presence of glucose inthe fluid further comprises: defining a sequence of windows in time; forat least one of said windows, computing a dispersion of said rate ofchange; and determining at least one of said sensitivity metrics based,at least in part, on said computed dispersion. In yet anotherimplementation, determining the at least one of the sensitivity metricsfurther comprises determining the at least one of the sensitivitymetrics based, at least in part, on a ratio of the computed dispersionand a mean value of glucose sensor measurements obtained in the at leastone of said windows.

In another particular implementation, an apparatus comprises: a glucosesensor to obtain measurements responsive to a presence of glucose in afluid; and a processor to: detect a change in responsiveness of saidglucose sensor to the presence of glucose in the fluid based, at leastin part, on an application of one or more thresholds to one or moresensitivity metrics, at least one of said sensitivity metrics beingcomputed based, at least part, on the measurements. In a particularexample implementation, the change in responsiveness comprises adecrease in sensitivity of said glucose to the presence of glucose inthe fluid. In another implementation, at least one of the sensitivitymetrics comprises a measurement of dispersion of a rate change in theblood glucose sensor measurements over at least a portion of the timeinterval. In another example implementation, the measurement ofdispersion of the rate of change comprises a variance of the rate ofchange. In another implementation, the change in responsiveness of theglucose sensor to the presence of glucose in said fluid is detected by:defining a sequence of windows in time; for at least one of the windows,computing a dispersion of the rate of change; and determining at leastone of the sensitivity metrics based, at least in part, on the computeddispersion. In yet another implementation, the at least one of thesensitivity metrics are detected by determining the at least one of thesensitivity metrics based, at least in part, on a ratio of said computeddispersion and a mean value of glucose sensor measurements obtained inthe at least one of the windows.

In another particular implementation, an article comprises anon-transitory storage medium having machine-readable instructionsstored thereon which are executable by a special purpose computingapparatus to: obtain a plurality of glucose sensor measurements at aglucose sensor over a time interval; and detect a change inresponsiveness of the glucose sensor to a presence of glucose in a fluidbased, at least in part, on an application of one or more thresholds toone or more sensitivity metrics, at least one of the sensitivity metricsbeing based, at least in part, on the glucose sensor measurements. In anexample implementation, the change in responsiveness comprises adecrease in sensitivity of the glucose sensor to the presence of glucosein the fluid. In an example implementation, at least one of thesensitivity metrics comprises a measurement of dispersion of a ratechange in the blood glucose sensor measurements over at least a portionof the time interval. In another implementation, the measurement ofdispersion of the rate of change comprises a variance of the rate ofchange. In another example implementation, the change in responsivenessof the glucose sensor to the presence of glucose in said fluid isdetected by: defining a sequence of windows in time; for at least one ofsaid windows, computing a dispersion of the rate of change; anddetermining at least one of the sensitivity metrics based, at least inpart, on the computed dispersion.

In yet another example implementation, an apparatus comprises: means forobtaining a plurality of glucose sensor measurements at a glucose sensorover a time interval; and means for detecting a change in responsivenessof said glucose sensor to a presence of glucose in a fluid based, atleast in part, on an application of one or more thresholds to one ormore sensitivity metrics, at least one of said sensitivity metrics beingbased, at least in part, on said glucose sensor measurements.

Other alternative example embodiments are described herein and/orillustrated in the accompanying Drawings. Additionally, particularexample embodiments may be directed to an article comprising a storagemedium including machine-readable instructions stored thereon which, ifexecuted by a special purpose computing device and/or processor, may bedirected to enable the special purpose computing device/processor toexecute at least a portion of described method(s) according to one ormore particular implementations. In other particular exampleembodiments, a sensor may be adapted to generate one or more signalsresponsive to a measured blood glucose concentration in a body while aspecial purpose computing device/processor may be adapted to perform atleast a portion of described method(s) according to one or moreparticular implementations based upon one or more signals generated bythe sensor.

BRIEF DESCRIPTION OF THE FIGURES

Non-limiting and non-exhaustive features are described with reference tothe following figures, wherein like reference numerals refer to likeand/or analogous parts throughout the various figures:

FIG. 1 is a schematic diagram of an example closed loop glucose controlsystem in accordance with an embodiment.

FIG. 2 is a front view of example closed loop hardware located on a bodyin accordance with an embodiment.

FIG. 3( a) is a perspective view of an example glucose sensor system foruse in accordance with an embodiment.

FIG. 3( b) is a side cross-sectional view of a glucose sensor system ofFIG. 3( a) for an embodiment.

FIG. 3( c) is a perspective view of an example sensor set for a glucosesensor system of FIG. 3( a) for use in accordance with an embodiment.

FIG. 3( d) is a side cross-sectional view of a sensor set of FIG. 3( c)for an embodiment.

FIG. 4 is a cross sectional view of an example sensing end of a sensorset of FIG. 3( d) for use in accordance with an embodiment.

FIG. 5 is a top view of an example infusion device with a reservoir doorin an open position, for use according to an embodiment.

FIG. 6 is a side view of an example infusion set with an insertionneedle pulled out, for use according to an embodiment.

FIG. 7 is a cross-sectional view of an example sensor set and an exampleinfusion set attached to a body in accordance with an embodiment.

FIG. 8( a) is a diagram of an example single device and its componentsfor a glucose control system in accordance with an embodiment.

FIG. 8( b) is a diagram of two example devices and their components fora glucose control system in accordance with an embodiment.

FIG. 8( c) is another diagram of two example devices and theircomponents for a glucose control system in accordance with anembodiment.

FIG. 8( d) is a diagram of three example devices and their componentsfor a glucose control system in accordance with an embodiment.

FIG. 9 is a schematic diagram of an example closed loop system tocontrol blood glucose levels via insulin infusion and/or glucagoninfusion using at least a controller based on glucose level feedback viaa sensor signal in accordance with an embodiment.

FIG. 10 is a schematic diagram of at least a portion of an examplecontroller including a sensor sensitivity analyzer in accordance with anembodiment.

FIG. 11 is a flow diagram of an example process for detecting sensorsensitivity drift in accordance with an embodiment.

FIG. 12 is a plot illustrating a change in sensitivity of a bloodglucose sensor according to an embodiment.

FIG. 13 is a plot of an estimated first derivative of a sensor signalsample as a function of signal sample amplitude and aggregated indistinct time intervals.

FIG. 14 is a plot of a first derivative of sensor measurement values asa function of sensor measurement values according to an embodiment.

FIGS. 15 and 16 are plots of sensor measurement values taken over timeand corresponding blood glucose concentration levels according to anembodiment.

FIG. 17 is a schematic diagram of an example controller that producesoutput information based on input data in accordance with an embodiment.

DETAILED DESCRIPTION

In an example glucose monitoring sensor and/or insulin delivery systemenvironment, measurements reflecting blood-glucose levels may beemployed in a closed loop infusion system for regulating a rate of fluidinfusion into a body. In particular example embodiments, a sensor and/orsystem may be adapted to regulate a rate of insulin and/or glucagoninfusion into a body of a patient based, at least in part, on a glucoseconcentration measurement taken from a body (e.g., from a blood-glucosesensor, including a current sensor). In certain example implementations,such a system may be designed to model a pancreatic beta cell (β-cell).Here, such a system may control an infusion device to release insulininto a body of a patient in an at least approximately similarconcentration profile as might be created by fully functioning humanβ-cells, if such were responding to changes in blood glucoseconcentrations in the body. Thus, such a closed loop infusion system maysimulate a body's natural insulin response to blood glucose levels.Moreover, it may not only make efficient use of insulin, but it may alsoaccount for other bodily functions as well because insulin can have bothmetabolic and mitogenic effects.

According to certain embodiments, examples of closed-loop systems asdescribed herein may be implemented in a hospital environment to monitorand/or control levels of glucose and/or insulin in a patient. Here, aspart of a hospital or other medical facility procedure, a caretaker orattendant may be tasked with interacting with a closed-loop system to,for example: enter blood-glucose reference measurement samples intocontrol equipment to calibrate blood glucose measurements obtained fromblood-glucose sensors, make manual adjustments to devices, and/or makechanges to therapies, just to name a few examples. Alternatively,according to certain embodiments, examples of closed-loop systems asdescribed herein may be implemented in non-hospital environments tomonitor and/or control levels of glucose and/or insulin in a patient.Here, a patient or other non-medical professional may be involved ininteracting with a closed-loop system.

However, while a closed-loop glucose control system is active, oversightby medical professionals, patients, non-medical professionals, etc. istypically reduced. Such a closed-loop glucose control system may becomeat least partially responsible for the health, and possibly thesurvival, of a diabetic patient. To more accurately control bloodglucose levels of a patient, a closed-loop system may be provided anobservation of a current blood glucose level. One approach to providingsuch an observation is implementation of a blood glucose sensor, such asincluding one or more such glucose sensors in a closed-loop system.

A closed-loop system may receive at least one glucose sensor signal fromone or more glucose sensors, with the glucose sensor signal intended toaccurately represent a current (or at least relatively current) bloodglucose level. If a glucose sensor signal indicates that a blood glucoselevel is currently too high, then a closed-loop system may takeaction(s) to lower the glucose level. On the other hand, if a glucosesensor signal indicates that a blood glucose level is currently too low,then a closed-loop system may take action(s) to raise the glucose level.Actions taken by a closed-loop system to control blood glucose levels ofa patient and protect the patient's health may therefore be based atleast partly on an accuracy and reliability of a glucose sensor signalreceived from a glucose sensor.

Unfortunately, a received glucose sensor signal may not be completelyreliable as a representation of a current blood glucose level of apatient. For example, a glucose sensor may gradually become increasinglyless capable of accurately measuring a current blood glucose level. Insuch situations, a glucose sensor signal that is received at acontroller of a closed-loop system may not be sufficiently reliable tojustify entrusting a patient's life and health to its control decisions.

In certain embodiments that are described herein, a closed loop systemmay assess a reliability of at least one sensor signal with respect toits ability to accurately reflect a blood glucose level of a patient.Among other things, a sensor may lose sensitivity to the presence ofblood glucose over time as a sensor is worn on a patient. In oneparticular embodiment, a change in a sensor's sensitivity toblood-glucose concentration may be detected based, at least in part, atleast in part, on an estimate of a dispersion of a rate change in bloodglucose sensor measurements over a time interval. If such a sensitivityis determined to decrease significantly, the sensor may be repaired orreplaced. In particular systems that employ multiple sensors,measurements from a sensor with diminished sensitivity may be discardedor de-weighted in computing an estimate of actual blood-glucoseconcentration. It should be understood, however, that this is merely anexample embodiment and that claimed subject matter is not limited inthis respect.

FIG. 1 is a block diagram of an example closed loop glucose controlsystem 5 in accordance with an embodiment. Particular embodiments mayinclude a glucose sensor system 10, a controller 12, an insulin deliverysystem 14, and a glucagon delivery system 15, etc. as shown in FIG. 1.In certain example embodiments, glucose sensor system 10 may generate asensor signal 16 representative of blood glucose levels 18 in body 20,and glucose sensor system 10 may provide sensor signal 16 to controller12. Controller 12 may receive sensor signal 16 and generate commands 22that are communicated at least to insulin delivery system 14 and/orglucagon delivery system 15. Insulin delivery system 14 may receivecommands 22 and infuse insulin 24 into body 20 in response to commands22. Likewise, glucagon delivery system 15 may receive commands 22 fromcontroller 12 and infuse glucagon 25 into body 20 in response tocommands 22.

Glucose sensor system 10 may include, by way of example but notlimitation, a glucose sensor; sensor electrical components to providepower to a glucose sensor and to generate sensor signal 16; a sensorcommunication system to carry sensor signal 16 to controller 12; asensor system housing for holding, covering, and/or containingelectrical components and a sensor communication system; any combinationthereof, and so forth.

Controller 12 may include, by way of example but not limitation,electrical components, other hardware, firmware, and/or software, etc.to generate commands 22 for insulin delivery system 14 and/or glucagondelivery system 15 based at least partly on sensor signal 16. Controller12 may also include a controller communication system to receive sensorsignal 16 and/or to provide commands 22 to insulin delivery system 14and/or glucagon delivery system 15. In particular exampleimplementations, controller 12 may include a user interface and/oroperator interface (e.g., a human interface as shown in FIG. 9)comprising a data input device and/or a data output device. Such a dataoutput device may, for example, generate signals to initiate an alarmand/or include a display or printer for showing a status of controller12 and/or a patient's vital indicators, monitored historical data,combinations thereof, and so forth. Such a data input device maycomprise dials, buttons, pointing devices, manual switches, alphanumerickeys, a touch-sensitive display, combinations thereof, and/or the likefor receiving user and/or operator inputs. It should be understood,however, that these are merely examples of input and output devices thatmay be a part of an operator and/or user interface and that claimedsubject matter is not limited in these respects.

Insulin delivery system 14 may include an infusion device and/or aninfusion tube to infuse insulin 24 into body 20. Similarly, glucagondelivery system 15 may include an infusion device and/or an infusiontube to infuse glucagon 25 into body 20. In alternative embodiments,insulin 24 and glucagon 25 may be infused into body 20 using a sharedinfusion tube. In other alternative embodiments, insulin 24 and/orglucagon 25 may be infused using an intravenous system for providingfluids to a patient (e.g., in a hospital or other medical environment).When an intravenous system is employed, glucose may be infused directlyinto a bloodstream of a body instead of or in addition to infusingglucagon into interstitial tissue. It should also be understood thatcertain example embodiments for closed loop glucose control system 5 mayinclude an insulin delivery system 14 without a glucagon delivery system15 (or vice versa).

In particular example embodiments, an infusion device (not explicitlyidentified in FIG. 1) may include infusion electrical components toactivate an infusion motor according to commands 22; an infusioncommunication system to receive commands 22 from controller 12; aninfusion device housing (not shown) to hold, cover, and/or contain theinfusion device; any combination thereof; and so forth.

In particular example embodiments, controller 12 may be housed in aninfusion device housing, and an infusion communication system maycomprise an electrical trace or a wire that carries commands 22 fromcontroller 12 to an infusion device. In alternative embodiments,controller 12 may be housed in a sensor system housing, and a sensorcommunication system may comprise an electrical trace or a wire thatcarries sensor signal 16 from sensor electrical components to controllerelectrical components. In other alternative embodiments, controller 12may have its own housing or may be included in a supplemental device. Inyet other alternative embodiments, controller 12 may be co-located withan infusion device and a sensor system within one shared housing. Infurther alternative embodiments, a sensor, a controller, and/or infusioncommunication systems may utilize a cable; a wire; a fiber optic line;RF, IR, or ultrasonic transmitters and receivers; combinations thereof;and/or the like instead of electrical traces, just to name a fewexamples.

Overview of Example Systems

FIGS. 2-6 illustrate example glucose control systems in accordance withcertain embodiments. FIG. 2 is a front view of example closed loophardware located on a body in accordance with certain embodiments. FIGS.3( a)-3(d) and 4 show different views and portions of an example glucosesensor system for use in accordance with certain embodiments. FIG. 5 isa top view of an example infusion device with a reservoir door in anopen position in accordance with certain embodiments. FIG. 6 is a sideview of an example infusion set with an insertion needle pulled out inaccordance with certain embodiments.

Particular example embodiments may include a sensor 26, a sensor set 28,a telemetered characteristic monitor 30, a sensor cable 32, an infusiondevice 34, an infusion tube 36, and an infusion set 38, any or all ofwhich may be worn on a body 20 of a user or patient, as shown in FIG. 2.As shown in FIGS. 3( a) and 3(b), telemetered characteristic monitor 30may include a monitor housing 31 that supports a printed circuit board33, battery or batteries 35, antenna (not shown), a sensor cableconnector (not shown), and so forth. A sensing end 40 of sensor 26 mayhave exposed electrodes 42 that may be inserted through skin 46 into asubcutaneous tissue 44 of a user's body 20, as shown in FIGS. 3( d) and4. Electrodes 42 may be in contact with interstitial fluid (ISF) that isusually present throughout subcutaneous tissue 44.

Sensor 26 may be held in place by sensor set 28, which may be adhesivelysecured to a user's skin 46, as shown in FIGS. 3( c) and 3(d). Sensorset 28 may provide for a connector end 27 of sensor 26 to connect to afirst end 29 of sensor cable 32. A second end 37 of sensor cable 32 mayconnect to monitor housing 31. Batteries 35 that may be included inmonitor housing 31 provide power for sensor 26 and electrical components39 on printed circuit board 33. Electrical components 39 may samplesensor signal 16 (e.g., of FIG. 1) and store digital sensor values(Dsig) in a memory. Digital sensor values Dsig may be periodicallytransmitted from a memory to controller 12, which may be included in aninfusion device.

With reference to FIGS. 2 and 5 (and FIG. 1), a controller 12 mayprocess digital sensor values Dsig and generate commands 22 (e.g., ofFIG. 1) for infusion device 34. Infusion device 34 may respond tocommands 22 and actuate a plunger 48 that forces insulin 24 (e.g., ofFIG. 1) out of a reservoir 50 that is located inside an infusion device34. Glucose may be infused from a reservoir responsive to commands 22using a similar and/or analogous device (not shown). In alternativeimplementations, glucose may be administered to a patient orally.

In particular example embodiments, a connector tip 54 of reservoir 50may extend through infusion device housing 52, and a first end 51 ofinfusion tube 36 may be attached to connector tip 54. A second end 53 ofinfusion tube 36 may connect to infusion set 38 (e.g., of FIGS. 2 and6). With reference to FIG. 6 (and FIG. 1), insulin 24 (e.g., of FIG. 1)may be forced through infusion tube 36 into infusion set 38 and intobody 16 (e.g., of FIG. 1). Infusion set 38 may be adhesively attached toa user's skin 46. As part of infusion set 38, a cannula 56 may extendthrough skin 46 and terminate in subcutaneous tissue 44 to completefluid communication between a reservoir 50 (e.g., of FIG. 5) andsubcutaneous tissue 44 of a user's body 16.

In example alternative embodiments, as pointed out above, a closed-loopsystem in particular implementations may be a part of a hospital-basedglucose management system. Given that insulin therapy during intensivecare has been shown to dramatically improve wound healing and reduceblood stream infections, renal failure, and polyneuropathy mortality,irrespective of whether subjects previously had diabetes (See, e.g., Vanden Berghe G. et al. NEJM 345: 1359-67, 2001), particular exampleimplementations may be used in a hospital setting to control a bloodglucose level of a patient in intensive care. In such alternativeembodiments, because an intravenous (IV) hookup may be implanted into apatient's arm while the patient is in an intensive care setting (e.g.,ICU), a closed loop glucose control may be established that piggy-backsoff an existing IV connection. Thus, in a hospital or othermedical-facility based system, IV catheters that are directly connectedto a patient's vascular system for purposes of quickly delivering IVfluids, may also be used to facilitate blood sampling and directinfusion of substances (e.g., insulin, glucose, anticoagulants, etc.)into an intra-vascular space.

Moreover, glucose sensors may be inserted through an IV line to provide,e.g., real-time glucose levels from the blood stream. Therefore,depending on a type of hospital or other medical-facility based system,such alternative embodiments may not necessarily utilize all of thedescribed system components. Examples of components that may be omittedinclude, but are not limited to, sensor 26, sensor set 28, telemeteredcharacteristic monitor 30, sensor cable 32, infusion tube 36, infusionset 38, and so forth. Instead, standard blood glucose meters and/orvascular glucose sensors, such as those described in co-pending U.S.Patent Application Publication No. 2008/0221509 (U.S. patent applicationSer. No. 12/121,647; to Gottlieb, Rebecca et al.; entitled “MULTILUMENCATHETER”), filed May 15, 2008, may be used to provide blood glucosevalues to an infusion pump control, and an existing IV connection may beused to administer insulin to an patient. Other alternative embodimentsmay also include fewer, more, and/or different components than thosethat are described herein and/or illustrated in the accompanyingDrawings.

Example System and/or Environmental Delays

Example system and/or environmental delays are described herein.Ideally, a sensor and associated component(s) would be capable ofproviding a real time, noise-free measurement of a parameter, such as ablood glucose measurement, that a control system is intended to control.However, in real-world implementations, there are typicallyphysiological, chemical, electrical, algorithmic, and/or other sourcesof time delays that cause a sensor measurement to lag behind an actualpresent value. Also, as noted herein, such a delay may arise from, forinstance, a particular level of noise filtering that is applied to asensor signal.

FIG. 7 is a cross-sectional view of an example sensor set and an exampleinfusion set that is attached to a body in accordance with anembodiment. In particular example implementations, as shown in FIG. 7, aphysiological delay may arise from a time that transpires while glucosemoves between blood plasma 420 and interstitial fluid (ISF). Thisexample delay may be represented by a circled double-headed arrow 422.As discussed above with reference to FIG. 2-6, a sensor may be insertedinto subcutaneous tissue 44 of body 20 such that electrode(s) 42 (e.g.,of FIGS. 3 and 4) near a tip, or sending end 40, of sensor 26 are incontact with ISF. However, a parameter to be measured may include aconcentration of glucose in blood.

Glucose may be carried throughout a body in blood plasma 420. Through aprocess of diffusion, glucose may move from blood plasma 420 into ISF ofsubcutaneous tissue 44 and vice versa. As blood glucose level 18 (e.g.,of FIG. 1) changes, so does a glucose level of ISF. However, a glucoselevel of ISF may lag behind blood glucose level 18 due to a timerequired for a body to achieve glucose concentration equilibrium betweenblood plasma 420 and ISF. Some studies have shown that glucose lag timesbetween blood plasma and ISF may vary between, e.g., 0 to 30 minutes.Some parameters that may affect such a glucose lag time between bloodplasma and ISF are an individual's metabolism, a current blood glucoselevel, whether a glucose level is rising or falling, combinationsthereof, and so forth, just to name a few examples.

A chemical reaction delay 424 may be introduced by sensor responsetimes, as represented by a circle 424 that surrounds a tip of sensor 26in FIG. 7. Sensor electrodes 42 (e.g., of FIGS. 3 and 4) may be coatedwith protective membranes that keep electrodes 42 wetted with ISF,attenuate the glucose concentration, and reduce glucose concentrationfluctuations on an electrode surface. As glucose levels change, suchprotective membranes may slow the rate of glucose exchange between ISFand an electrode surface. In addition, there may be chemical reactiondelay(s) due to a reaction time for glucose to react with glucoseoxidase GOX to generate hydrogen peroxide and a reaction time for asecondary reaction, such as a reduction of hydrogen peroxide to water,oxygen, and free electrons.

Thus, an insulin delivery delay may be caused by a diffusion delay,which may be a time for insulin that has been infused into a tissue todiffuse into the blood stream. Other contributors to insulin deliverydelay may include, but are not limited to: a time for a delivery systemto deliver insulin to a body after receiving a command to infuseinsulin; a time for insulin to spread throughout a circulatory systemonce it has entered the blood stream; and/or by other mechanical,electrical/electronic, or physiological causes alone or in combination,just to name a few examples. In addition, a body clears insulin evenwhile an insulin dose is being delivered from an insulin delivery systeminto the body. Because insulin is continuously cleared from blood plasmaby a body, an insulin dose that is delivered to blood plasma too slowlyor is delayed is at least partially, and possibly significantly, clearedbefore the entire insulin dose fully reaches blood plasma. Therefore, aninsulin concentration profile in blood plasma may never achieve a givenpeak (nor follow a given profile) that it may have achieved if therewere no delay.

Moreover, there may also be a processing delay as an analog sensorsignal Isig is converted to digital sensor values Dsig. In particularexample embodiments, an analog sensor signal Isig may be integrated overone-minute intervals and converted to a number of counts. Thus, in sucha case, an analog-to-digital (A/D) conversion time may result in anaverage delay of 30 seconds. In particular example embodiments,one-minute values may be averaged into 5-minute values before they areprovided to controller 12 (e.g., of FIG. 1). A resulting average delaymay be two-and-one-half minutes (e.g., half of the averaging interval).In example alternative embodiments, longer or shorter integration timesmay be used that result in longer or shorter delay times.

In other example embodiments, an analog sensor signal current Isig maybe continuously converted to an analog voltage Vsig, and an A/Dconverter may sample voltage Vsig every 10 seconds. Thus, in such acase, six 10-second values may be pre-filtered and averaged to create aone-minute value. Also, five one-minute values may be filtered andaveraged to create a five-minute value that results in an average delayof two-and-one-half minutes. In other alternative embodiments, othersensor signals from other types of sensors may be converted to digitalsensor values Dsig as appropriate before transmitting the digital sensorvalues Dsig to another device. Moreover, other embodiments may use otherelectrical components, other sampling rates, other conversions, otherdelay periods, a combination thereof, and so forth.

System Configuration Examples

FIG. 8( a)-8(d) illustrate example diagrams of one or more devices andtheir components for glucose control systems in accordance with certainembodiments. These FIG. 8( a)-8(d) show exemplary, but not limiting,illustrations of components that may be utilized with certaincontroller(s) that are described herein above. Various changes incomponents, layouts of such components, combinations of elements, and soforth may be made without departing from the scope of claimed subjectmatter.

Before it is provided as an input to controller 12 (e.g., of FIG. 1), asensor signal 16 may be subjected to signal conditioning such aspre-filtering, filtering, calibrating, and so forth, just to name a fewexamples. Components such as a pre-filter, one or more filters, acalibrator, controller 12, etc. may be separately partitioned orphysically located together (e.g., as shown in FIG. 8( a)), and they maybe included with a telemetered characteristic monitor transmitter 30, aninfusion device 34, a supplemental device, and so forth.

In particular example embodiments, a pre-filter, filter(s), and acalibrator may be included as part of telemetered characteristic monitortransmitter 30, and a controller (e.g., controller 12) may be includedwith infusion device 34, as shown in FIG. 8( b). In example alternativeembodiments, a pre-filter may be included with telemeteredcharacteristic monitor transmitter 30, and a filter and calibrator maybe included with a controller in an infusion device, as shown in FIG. 8(c). In other alternative example embodiments, a pre-filter may beincluded with telemetered characteristic monitor transmitter 30, whilefilter(s) and a calibrator are included in supplemental device 41, and acontroller may be included in the infusion device, as shown in FIG. 8(d).

In particular example embodiments, a sensor system may generate amessage that includes information based on a sensor signal such asdigital sensor values, pre-filtered digital sensor values, filtereddigital sensor values, calibrated digital sensor values, commands, andso forth, just to name a few examples. Such a message may include othertypes of information as well, including, by way of example but notlimitation, a serial number, an ID code, a check value, values for othersensed parameters, diagnostic signals, other signals, and so forth. Inparticular example embodiments, digital sensor values Dsig may befiltered in a telemetered characteristic monitor transmitter 30, andfiltered digital sensor values may be included in a message sent toinfusion device 34 where the filtered digital sensor values may becalibrated and used in a controller. In other example embodiments,digital sensor values Dsig may be filtered and calibrated beforetransmission to a controller in infusion device 34. Alternatively,digital sensor values Dsig may be filtered, calibrated, and used in acontroller to generate commands 22 that are sent from telemeteredcharacteristic monitor transmitter 30 to infusion device 34.

In further example embodiments, additional components, such as apost-calibration filter, a display, a recorder, a blood glucose meter,etc. may be included in devices with any of the other components, orthey may stand-alone. If a blood glucose meter is built into a device,for instance, it may be co-located in the same device that contains acalibrator. In alternative example embodiments, more, fewer, and/ordifferent components may be implemented than those that are shown inFIG. 8 and/or described herein above.

In particular example embodiments, RF telemetry may be used tocommunicate between devices that contain one or more components, such astelemetered characteristic monitor transmitter 30 and infusion device34. In alternative example embodiments, other communication mediums maybe employed between devices, such as wireless wide area network (WAN)(e.g., cell communication), Wi-Fi, wires, cables, IR signals, lasersignals, fiber optics, ultrasonic signals, and so forth, just to name afew examples.

FIG. 9 is a schematic diagram of an example closed loop system 900 tocontrol blood glucose levels via insulin infusion and/or glucagoninfusion using at least a controller based on glucose level feedback viaa sensor signal in accordance with an embodiment. In particular exampleembodiments, a closed loop control system may be used for deliveringinsulin to a body to compensate for β-cells that perform inadequately.There may be a desired basal blood glucose level G_(B) for a particularbody. A difference between a desired basal blood glucose level G_(B) andan estimate of a present blood glucose level G is the glucose levelerror G_(E) that may be corrected. For particular example embodiments,glucose level error G_(E) may be provided as an input to controller 12,as shown in FIG. 9. Although at least a portion of controller 12 may berealized as a proportional-integral-derivative (PID) controller, claimedsubject matter is not so limited, and controller 12 may be realized inalternative manners.

If glucose level error G_(E) is positive (meaning, e.g., that a presentestimate of blood glucose level G is higher than a desired basal bloodglucose level G_(B)), then a command from controller 12 may generate acommand 22 to drive insulin delivery system 34 to provide insulin 24 tobody 20. Insulin delivery system 34 may be an example implementation ofinsulin delivery system 14 (e.g., of FIG. 1). Likewise, if G_(E) isnegative (meaning, e.g., that a present estimate of blood glucose levelG is lower than a desired basal blood glucose level G_(B)), then acommand from controller 12 may generate a command 22 to drive glucagondelivery system 35 to provide glucagon 25 to body 20. Glucagon deliverysystem 35 may be an example implementation of glucagon delivery system15 (e.g., of FIG. 1).

Closed loop system 900 may also include and/or be in communication witha human interface 65. Example implementations for a human interface 65are described herein above with particular reference to FIG. 1 in thecontext of an output device. As shown, human interface 65 may receiveone or more commands 22 from controller 12. Such commands 22 mayinclude, by way of example but not limitation, one or more commands tocommunicate information to a user (e.g., a patient, a healthcareprovider, etc.) visually, audibly, haptically, some combination thereof,and so forth. Such information may include data, an alert, or some othernotification 55. Human interface 65 may include a screen, a speaker, avibration mechanism, any combination thereof, and so forth, just to namea few examples. Hence, in response to receiving a command 22 fromcontroller 12, human interface 65 may present at least one notification55 to a user via a screen, a speaker, a vibration, and so forth.

In terms of a control loop for purposes of discussion, glucose may beconsidered to be positive, and therefore insulin may be considered to benegative. Sensor 26 may sense an ISF glucose level of body 20 andgenerate a sensor signal 16. For certain example embodiments, a controlloop may include a filter and/or calibration unit 456 and/or correctionalgorithm(s) 454. However, this is by way of example only, and claimedsubject matter is not so limited. Sensor signal 16 may be filtered/orand calibrated at unit 456 to create an estimate of present bloodglucose level 452. Although shown separately, filter and/or calibrationunit 456 may be integrated with controller 12 without departing fromclaimed subject matter. Moreover, filter and/or calibration unit 456 mayalternatively be realized as part of controller 12 (or vice versa)without departing from claimed subject matter.

In particular example embodiments, an estimate of present blood glucoselevel G may be adjusted with correction algorithms 454 before it iscompared with a desired basal blood glucose level G_(B) to calculate anew glucose level error G_(E) to start a loop again. Also, an attendant,a caretaker, a patient, etc. may obtain blood glucose reference samplemeasurements from a patient's blood using, e.g., glucose test strips.These blood-based sample measurements may be used to calibrate ISF-basedsensor measurements, e.g. using techniques such as those described inU.S. Pat. No. 6,895,263, issued May 17, 2005, and/or other techniques.Although shown separately, a correction algorithms unit 454 may beintegrated with controller 12 without departing from claimed subjectmatter. Moreover, correction algorithms unit 454 may alternatively berealized as part of controller 12 (or vice versa) without departing fromclaimed subject matter. Similarly, a difference unit and/or otherfunctionality for calculating G_(E) from G and G_(B) may be incorporatedas part of controller 12 without departing from claimed subject matter.

For an example PID-type of controller 12, if a glucose level error G_(E)is negative (meaning, e.g., that a present estimate of blood glucoselevel is lower than a desired basal blood glucose level G_(B)), thencontroller 12 may reduce or stop insulin delivery depending on whetheran integral component response of a glucose error G_(E) is stillpositive. In alternative embodiments, as discussed below, controller 12may initiate infusion of glucagon 25 if glucose level error G_(E) isnegative. If a glucose level error G_(E) is zero (meaning, e.g., that apresent estimate of blood glucose level is equal to a desired basalblood glucose level G_(B)), then controller 12 may or may not issuecommands to infuse insulin 24 or glucagon 25, depending on a derivativecomponent (e.g., whether a glucose level is rising or falling) and/or anintegral component (e.g., how long and by how much a glucose level hasbeen above or below basal blood glucose level G_(B)).

To more clearly understand the effects that a body has on such a controlloop, a more detailed description of example physiological effects thatinsulin may have on glucose concentration in ISF is provided. Inparticular example embodiments, infusion delivery system 34 may deliverinsulin into ISF of subcutaneous tissue 44 (e.g., also of FIGS. 3, 4,and 6) of body 20. Alternatively, insulin delivery system 34 or aseparate infusion device (e.g., glucagon delivery system 35) maysimilarly deliver glucose and/or glucagon into ISF of subcutaneoustissue 44. Here, insulin 24 may diffuse from local ISF surrounding acannula into blood plasma and spread throughout body 20 in a maincirculatory system (e.g., as represented by blood stream 47). Infusedinsulin may diffuse from blood plasma into ISF substantially throughoutthe entire body.

Here in the body, insulin 24 may bind with and activate membranereceptor proteins on cells of body tissues. This may facilitate glucosepermeation into activated cells. In this way, tissues of body 20 maytake up glucose from ISF. As ISF glucose level decreases, glucose maydiffuse from blood plasma into ISF to maintain glucose concentrationequilibrium. Glucose in ISF may permeate a sensor membrane of sensor 26and affect sensor signal 16.

In addition, insulin may have direct and indirect effects on liverglucose production. Typically, increased insulin concentration maydecrease liver glucose production. Therefore, acute and immediateinsulin response may not only help a body to efficiently take upglucose, but it may also substantially stop a liver from adding toglucose in the blood stream. In alternative example embodiments, aspointed out above, insulin and/or glucose may be delivered more directlyinto the blood stream instead of into ISF, such as by delivery intoveins, arteries, the peritoneal cavity, and so forth, just to name a fewexamples. Accordingly, any time delay associated with moving insulinand/or glucose from ISF into blood plasma may be diminished. In otheralternative example embodiments, a glucose sensor may be in contact withblood or other body fluids instead of ISF, or a glucose sensor may beoutside of a body such that it may measure glucose through anon-invasive means. Embodiments using alternative glucose sensors mayhave shorter or longer delays between an actual blood glucose level anda measured blood glucose level.

A continuous glucose measuring sensor (CGMS) implementation for sensor26, for example, may detect a glucose concentration in ISF and provide aproportional current signal. A current signal (isig) may be linearlycorrelated with a reference blood glucose concentration (BG). Hence, alinear model, with two parameters (e.g., slope and offset), may be usedto calculate a sensor glucose concentration (SG) from sensor currentisig.

One or more controller gains may be selected so that commands from acontroller 12 direct infusion device 34 to release insulin 24 into body20 at a particular rate. Such a particular rate may cause insulinconcentration in blood to follow a similar concentration profile aswould be caused by fully functioning human β-cells responding to bloodglucose concentrations in a body. Similarly, controller gain(s) may beselected so that commands 22 from controller 12 direct an infusiondevice of glucagon delivery system 35 to release glucagon 25 in responseto insulin excursions. In particular example embodiments, controllergains may be selected at least partially by observing insulinresponse(s) of several normal glucose tolerant (NGT) individuals havinghealthy, normally-functioning β-cells.

In one or more example implementations, a system may additionallyinclude a communication unit 458. A communication unit 458 may comprise,by way of example but not limitation, a wireless wide area communicationmodule (e.g., a cell modem), a transmitter and/or a receiver (e.g., atransceiver), a Wi-Fi or Bluetooth chip or radio, some combinationthereof, and so forth. Communication unit 458 may receive signals from,by way of example but not limitation, filter and/or calibration unit456, sensor 26 (e.g., sensor signal 16), controller 12 (e.g. commands22), any combination thereof, and so forth. Although not specificallyshown in FIG. 9, communication unit 458 may also receive signals fromother units (e.g., correction algorithms unit 454, a delivery system 34and/or 35, human interface 65, etc.). Also, communication unit 458 maybe capable of providing signals to any of the other units of FIG. 9(e.g., controller 12, filter and/or calibration unit 456, humaninterface 65, etc.). Communication unit 458 may also be integrated withor otherwise form a part of another unit, such as controller 12 orfilter and/or calibration unit 456.

Communication unit 458 may be capable of transmitting calibrationoutput; calibration failure alarms; control algorithm states; sensorsignal alerts; and/or other physiological, hardware, and/or softwaredata (e.g., diagnostic data); and so forth to a remote data center foradditional processing and/or storage (e.g., for remote telemetrypurposes). These transmissions can be performed in response todiscovered/detected conditions, automatically, semi-automatically (e.g.,at the request of the remote data center), manually at the request ofthe patient, any combination thereof, and so forth, just to provide afew examples. The data can be subsequently served on request to remoteclients including, but not limited to, mobile phones, physician'sworkstations, patient's desktop computers, any combination of the above,and so forth, just to name a few examples. Communication unit 458 mayalso be capable of receiving from a remote location various information,including but not limited to: calibration information, instructions,operative parameters, other control information, some combinationthereof, and so forth. Such control information may be provided fromcommunication unit 458 to other system unit(s) (e.g., controller 12,filter and/or calibration unit 456, etc.).

As pointed out above, in certain example implementations a continuousglucose monitoring sensor may measure glucose concentration in ISF byoxidizing localized glucose with the help of a glucose-oxidizing enzyme.A sensor output signal may comprise a current signal (ISIG, nAmps) whichmay be at least roughly an increasing function of a glucoseconcentration in ISF. Under particular conditions (e.g., accumulation ofcontaminants, etc.), a sensor's sensitivity in responding to a bloodglucose concentration may diminish with normal use over time.Eventually, a sensitivity of a glucose sensor may diminish to a pointwhere the sensor becomes unreliable and should be replaced.

FIG. 10 is a schematic diagram of at least a portion of an examplecontroller 12 including a sensor signal reliability analyzer 1002. Asillustrated, controller 12 may include a sensor sensitivity analyzer1002, and controller 12 may include or have access to a series ofsamples 1004 and may produce at least one alert signal 1006.

For certain example embodiments, series of samples 1004 may comprisemultiple samples taken from a sensor signal 16 (e.g., also of FIGS. 1and 9) at multiple sampling times. Thus, series of samples 1004 mayinclude multiple samples of at least one sensor signal, such as sensorsignal 16, and may be responsive to a blood glucose level of a patient.

Sensor sensitivity analyzer 1002 may consider one or more facets ofseries of samples 1004 to assess sensor sensitivity in responding to apresence of glucose in blood or interstitial fluid. Based at leastpartly on such assessment(s), sensor sensitivity analyzer 1002 mayproduce at least one alert signal 1006. In one embodiment, an alertsignal 1006 may be issued in response to an assessment indicating that asensor signal may not be sufficiently reliable (e.g., sufficientlysensitive in responding to a presence of glucose in blood orinterstitial fluid) so as to justify entrusting a patient's health toclosed-loop glucose control decisions that are based on such anunreliable sensor signal. Such an indication may be responsive to, forexample, an indication that a sensitivity of a sensor to the presence ofglucose in bodily fluid has degraded significantly. In exampleimplementations, an alert signal 1006 may comprise at least one command22 (e.g., also of FIGS. 1 and 9) that is issued from controller 12. Forinstance, an alert signal 1006 may be provided to a human interface 65(e.g., of FIG. 9) and/or an insulin delivery system 34 (e.g., of FIG.9). Alternatively and/or additionally, an alert signal 1006 may beprovided to another component and/or unit of (e.g., that is internal of)controller 12.

FIG. 11 is a flow diagram 1200 of an example method for assessingoperation of a glucose sensor in accordance with an embodiment. Althoughoperations 1202-1208 are shown and described in a particular order, itshould be understood that methods may be performed in alternative ordersand/or manners (including with a different number of operations) withoutdeparting from claimed subject matter. At least some operation(s) offlow diagram 1200 may be performed so as to be fully or partiallyoverlapping with other operation(s). Additionally, although thedescription below may reference particular aspects and featuresillustrated in certain other figures, methods may be performed withother aspects and/or features.

For certain example implementations, at operation 1202, a series ofsamples of at least one sensor signal that is responsive to a bloodglucose level of a patient may be obtained. At operation 1204, at leastone sensitivity metric may be determined, based at least partly on theseries of samples of the at least one sensor signal, to characterizechanges in sensitivity in responding to the presence of glucose in abodily fluid such as blood. Here, in a particular implementation, and asexplained below, such a sensitivity metric may be computed based, atleast in part, on a measured or estimated dispersion of a rate of changeof sensor signal measurements with respect to time (e.g., a firstderivative of ISIG dIsig/dt being just one non-limiting example of arate of change in sensor signal measurements) over a time period.Another sensitivity metric may be determined based, at least in part ona mean value for ISIG over such a time period.

At operation 1206, an alert signal may be generated responsive to acomparison of the at least one sensitivity metric with at least onepredetermined threshold. For example, a measured or estimated dispersionof dIsig/dt may be compared with a first threshold and/or a mean valuefor ISIG may be compared with a second threshold. Alternatively, aweighted (e.g., time-weighted) average of ISIG may be compared with sucha threshold. Use of a time-weighted average may allow for use of longerwindows while still emphasizing more recent measurements. Here,weighting may be applied in a single-tailed fashion, where the morerecent data is weighted more heavily than older data within a particularwindow. Particular weighting functions may include, for example, anexponential decay or one-sided Gaussian distribution. Particularconditions that may be indicative of a sensor's decreased sensitivity inresponding to the presence of glucose are as discussed above. In anexample implementation, an alert may be generated by initiating a signalto indicate to a blood glucose controller that the sensor's sensitivityto a presence of glucose may have decreased substantially.

At operation 1208, an insulin infusion treatment for the patient may bealtered responsive at least partly to the assessed reliability of the atleast one sensor signal. For example, an insulin infusion treatment fora patient may be altered by changing (e.g., increasing or decreasing) anamount of insulin being infused, by ceasing an infusion of insulin, bydelaying infusion until more samples are taken, by switching to adifferent sensor, by switching to a manual mode, by changing a relativeweighting applied to a given sensor or sensors and/or the samplesacquired there from, any combination thereof, and so forth, just to namea few examples.

As discussed above, Isig may represent current measurements which arereflective of a blood glucose concentration. FIG. 15 plots the behaviorof ISIG in a patient over a time period spanning several days for bloodglucose concentration levels shown in FIG. 16. As can be observed, arange from troughs to peaks decreases over time, indicating a decreasedsensitivity in responsiveness to a presence of blood glucose. FIG. 14 isa plot of dIsig/dt for discrete ISIG values in FIG. 15. A decreasedsensitivity in a glucose sensor in responding to a presence of glucosemay be more clearly illustrated in FIGS. 12 and 13. Here, FIG. 12 is aplot illustrating a change in sensitivity of a glucose sensor to thepresence of glucose in a fluid over a sequence of time windows. As shownin the particular example of FIG. 12, an amplitude of Isig may trendlower over time due to a decreased sensitivity of a sensor to thepresence of glucose in fluid. In a particular implementation, asensitivity of a sensor to the presence of glucose may decrease to thepoint where Isig values are no longer reliable for certain applicationssuch as providing reliable estimates or predictions of blood glucose ina closed-loop insulin delivery system.

As pointed out above, FIG. 12 shows a segmentation of operation timeinto distinct “windows” during which ISIG values may be processed forevaluating a sensitivity of a sensor in responding to the presence ofglucose in fluid (e.g., at operation 1204). As discussed below, ISIGvalues obtained in any particular window may be evaluated to determinewhether the sensor's sensitivity to the presence of glucose hasdecreased to the point that action is to be taken (e.g., at operation1206). In the particular embodiment of FIG. 12, such an evaluation ofISIG values may performed at each non-overlapping window as shown.Alternatively, implementation, a window for capturing Isig values forevaluation may comprise a sliding window. Here, the size of the windowmay be set so as to balance responsiveness to changes in sensitivity andaccuracy (e.g., by having a large number of data points. For example, ifa window size is too large, a detection of a loss of sensitivity may beunnecessarily delayed resulting in a delayed alarm to the user. On theother hand, if the window size is too small, a detection scheme may beoverly sensitive, resulting in false alarms. In one implementation, awindow size may be a particular meal period (e.g., ⅓ of the day assumingthree meals in a 24-hour period). A window size may also be set by analgorithm subject to an amount of time likely to reach particularthreshold conditions (e.g., value for ISIG). A window size may also beset according to a patient-specific meal cycle (e.g., from historicaldata) where the window is to be long enough to capture a patient'sglucose highs and lows throughout the day. For example, if a window isset at a very short period (e.g., 1.0 hour), hitting only highs or onlylows may trigger false alarms. A long-term running-window coveringseveral meal periods (e.g., one day) may be effective at measuring anestimated dispersion of dISIG/dt. Also, time windows for a particulartime of day on one day may be compared with the same time of day on adifferent day to detecting a change in sensitivity. FIG. 13 is a plot ofan estimated first derivative of a sensor signal sample as a function ofsignal sample amplitude and aggregated in distinct time intervalscorresponding to window1 through window4 shown in FIG. 12. As can beobserved by inspection, ISIG values obtained at the earliest window1tend to be at the highest levels. Also, dISIG/dt for ISIG valuesobtained at window1 show the greatest degree of dispersion, indicating ahigh sensitivity in responding to the presence of glucose. In contrast,dISIG/dt determined for ISIG values obtained at window4 show thesmallest degree of dispersion, indicating a diminished sensitivity inresponding to a presence of glucose.

In one particular implementation, for any particular window of ISIGmeasurements, a sensitivity metric (e.g., dispersion of dIsig/dt and/ormean value for ISIG) may be computed (e.g., at operation 1204) andcompared to a threshold (e.g. at operation 1206) for determining whetheraction should be taken.

Here, in a particular implementation, if the sensitivity metric fallsbelow a threshold level, indicating a reduced sensor sensitivity inresponding to the presence of blood glucose, one or more actions may betaken as discussed above. In a particular implementation, multiplesensitivity metrics may be derived (e.g., dispersion of dIsig/dt and/ormean value for ISIG) and different combinations of conditions may betested at operation 1206. For example, a signal indicating a substantialdecrease in sensitivity may be initiated in response to a combination ofboth a dispersion of dISIG/dt being less than a first threshold and meanvalue for ISIG being less than a second threshold. In the particularimplementation described above, a variance of dISIG/dt is used anindicator or measurement of a dispersion of dISIG/dt. While the aboveexample uses a variance of dISIG/dt is used an indicator or measurementof a dispersion of dISIG/dt, other metrics may be used such as acoefficient of variation, root mean of the squared error (RMSE),normalized RMSE or sum of the squared errors (SSE). It should beunderstood, however, that these are merely examples of how a dispersionof dISIG/dt, either a statistical dispersion or otherwise, may bequantified according to a particular implementation, and that claimedsubject matter is not limited in this respect.

A value for dISIG/dt may be computed using any one of severaltechniques. Techniques for determining or estimating dISIG/dt providedherein are merely example techniques, and it should be understood thatany of these techniques mentioned, or techniques not mentioned, may beused without deviating from claimed subject matter. Applying a finitedifference technique, a value for may be determined as follows:

dISIG(T)/dt=ISIG(T)−ISIG(T−k)]/(T−k),

where k is selected to filter noisy samples of ISIG.

Applying a Savitzky-Golay filter, as discussed in Savitzky, A; Golay, MJ E: Smoothing and differentiation of data by simplified least squaresprocedures, Analytical Chemistry 1964; 36 (8): 1627-1639, by performinga local polynomial regression of degree M on a series of values (e.g.,of at least M+1 values equally spaced), ISIG′(t) at discrete points maybe computed as follows:

$\begin{matrix}{g_{i} = {\sum\limits_{n = {i - N}}^{i}{c_{n}^{M}{ISIG}_{i + n}}}} & (7) \\{{\frac{{ISIG}}{t_{i}} = \frac{g_{i}}{\Delta}},} & (8)\end{matrix}$

where:

-   -   N>M and values for c represent sample Savitzky-Golay        coefficients.

In another particular implementation, Fourier decomposition may be usedto compute a first derivative in the frequency domain as discussed inJauberteau, F; Jauberteau, J L: Numerical differentiation with noisysignal, Applied Mathematics and Computation 2009; 215: 2283-2297. Apiecewise cubic spline interpolation may be used smooth values forISIG(t). Its Fourier coefficients may give an approximation of ISIG′(t).

FIG. 17 is a schematic diagram 1700 of an example controller 12 thatproduces output information 1712 based on input data 1710 in accordancewith an embodiment. As illustrated, controller 12 may include one ormore processors 1702 and at least one memory 1704. In certain exampleembodiments, memory 1704 may store or otherwise include instructions1706 and/or sensor sample data 1708. Sensor sample data 1708 mayinclude, by way of example but not limitation, blood glucose sensormeasurements, such as series of samples 1004.

Input data 1710 may include, for example, sensor measurements (e.g.,from an ISF current sensor). Output information 1712 may include, forexample, one or more commands, and such commands may include reportinginformation. Current sensor measurements of input data 1710 maycorrespond to sensor signal 16 and/or sampled values resulting therefrom. Commands of output information 1712 may correspond to commands 22,which may be derived from one or more alert signals 1006 (e.g., of FIG.10 and/or instructions or other information resulting there from.

In certain example embodiments, input data 1710 may be provided tocontroller 12. Based on input data 1710, controller 12 may produceoutput information 1712. Current sensor measurements that are receivedas input data 1710 may be stored as sensor sample data 1708. Controller12 may be programmed with instructions 1706 to perform algorithms,functions, methods, etc.; to implement attributes, features, etc.; andso forth that are described herein. For example, a controller 12 may beconfigured to perform the functions described herein with regard todetecting a change in sensitivity of a sensor in responding to apresence of glucose in a fluid. Controller 12 may therefore be coupledto at least one blood glucose sensor to receive one or more signalsbased on blood glucose sensor measurements.

A controller 12 that comprises one or more processors 1702 may executeinstructions 1706 to thereby render a controller unit a special purposecomputing device to perform algorithms, functions, methods, etc.; toimplement attributes, features, etc.; and so forth that are describedherein. Processor(s) 1702 may be realized as microprocessors, digitalsignal processors (DSPs), application specific integrated circuits(ASICs), programmable logic devices (PLDs), controllers,micro-controllers, a combination thereof, and so forth, just to name afew examples. Alternatively, an article may comprise at least onestorage medium (e.g., such as one or more memories) having storedthereon instructions 1706 that are executable by one or more processors.

Unless specifically stated otherwise, as is apparent from the precedingdiscussion, it is to be appreciated that throughout this specificationdiscussions utilizing terms such as “processing”, “computing”,“calculating”, “determining”, “assessing”, “estimating”, “identifying”,“obtaining”, “representing”, “receiving”, “transmitting”, “storing”,“analyzing”, “measuring”, “detecting”, “controlling”, “delaying”,“initiating”, “providing”, “performing”, “generating”, “altering” and soforth may refer to actions, processes, etc. that may be partially orfully performed by a specific apparatus, such as a special purposecomputer, special purpose computing apparatus, a similar special purposeelectronic computing device, and so forth, just to name a few examples.In the context of this specification, therefore, a special purposecomputer or a similar special purpose electronic computing device may becapable of manipulating or transforming signals, which are typicallyrepresented as physical electronic and/or magnetic quantities withinmemories, registers, or other information storage devices; transmissiondevices; display devices of a special purpose computer; or similarspecial purpose electronic computing device; and so forth, just to namea few examples. In particular example embodiments, such a specialpurpose computer or similar may comprise one or more processorsprogrammed with instructions to perform one or more specific functions.Accordingly, a special purpose computer may refer to a system or adevice that includes an ability to process or store data in the form ofsignals. Further, unless specifically stated otherwise, a process ormethod as described herein, with reference to flow diagrams orotherwise, may also be executed or controlled, in whole or in part, by aspecial purpose computer.

It should be understood that aspects described above are examples onlyand that embodiments may differ there from without departing fromclaimed subject matter. Also, it should be noted that although aspectsof the above systems, methods, apparatuses, devices, processes, etc.have been described in particular orders and in particular arrangements,such specific orders and arrangements are merely examples and claimedsubject matter is not limited to the orders and arrangements asdescribed. It should additionally be noted that systems, devices,methods, apparatuses, processes, etc. described herein may be capable ofbeing performed by one or more computing platforms.

In addition, instructions that are adapted to realize methods,processes, etc. that are described herein may be capable of being storedon a storage medium as one or more machine readable instructions. Ifexecuted, machine readable instructions may enable a computing platformto perform one or more actions. “Storage medium” as referred to hereinmay relate to media capable of storing information or instructions whichmay be operated on, or executed by, one or more machines (e.g., thatinclude at least one processor). For example, a storage medium maycomprise one or more storage articles and/or devices for storingmachine-readable instructions or information. Such storage articlesand/or devices may comprise any one of several media types including,for example, magnetic, optical, semiconductor, a combination thereof,etc. storage media. By way of further example, one or more computingplatforms may be adapted to perform one or more processes, methods, etc.in accordance with claimed subject matter, such as methods, processes,etc. that are described herein. However, these are merely examplesrelating to a storage medium and a computing platform and claimedsubject matter is not limited in these respects.

Although there have been illustrated and described what are presentlyconsidered to be example features, it will be understood by thoseskilled in the art that various other modifications may be made, andequivalents may be substituted, without departing from claimed subjectmatter. Additionally, many modifications may be made to adapt aparticular situation to the teachings of claimed subject matter withoutdeparting from central concepts that are described herein. Therefore, itis intended that claimed subject matter not be limited to particularexamples disclosed, but that such claimed subject matter may alsoinclude all aspects falling within the scope of appended claims, andequivalents thereof.

1. A method comprising: obtaining a plurality of glucose sensormeasurements at a glucose sensor over a time interval; and detecting achange in responsiveness of said glucose sensor to a presence of glucosein a fluid based, at least in part, on an application of one or morethresholds to one or more sensitivity metrics, at least one of saidsensitivity metrics being based, at least in part, on said glucosesensor measurements.
 2. The method of claim 1, wherein said change inresponsiveness comprises a decrease in sensitivity of said glucosesensor to the presence of glucose in said fluid.
 3. The method of claim1, wherein at least one of said sensitivity metrics comprises ameasurement of dispersion of a rate change in said blood glucose sensormeasurements over at least a portion of said time interval.
 4. Themethod of claim 3, wherein said measurement of dispersion of said rateof change comprises a variance of said rate of change.
 5. The method ofclaim 1, wherein at least one of said sensitivity metrics comprises acomputed mean value of said sensor measurements obtained over saidportion of said time interval.
 6. The method of claim 1, and furthercomprising: generating an alert signal in response to said detectedchange in said responsiveness.
 7. The method of claim 1, wherein saidportion of said time interval comprises a sliding time window.
 8. Themethod of claim 1, wherein said one or more sensitivity metrics compriseat least a measurement of dispersion of a rate change in said bloodglucose sensor measurements and a mean value of said sensor measurementsover at least a portion of said time interval, and wherein a decrease insensitivity of said sensor is detected if the measurement of dispersionof a rate change in said blood glucose sensor measurements does notexceed a first threshold and the mean value of said sensor measurementsdoes not exceed a second threshold.
 9. The method of claim 1, whereinsaid fluid comprises interstitial fluid.
 10. The method of claim 1,wherein said detecting said change in responsiveness of said glucosesensor to the presence of glucose in said fluid further comprises:defining a sequence of windows in time; for at least one of saidwindows, computing a dispersion of said rate of change; and determiningat least one of said sensitivity metrics based, at least in part, onsaid computed dispersion.
 11. The method of claim 10, whereindetermining said at least one of said sensitivity metrics furthercomprises determining said at least one of said sensitivity metricsbased, at least in part, on a ratio of said computed dispersion and amean value of glucose sensor measurements obtained in said at least oneof said windows.
 12. An apparatus comprising: a glucose sensor to obtainmeasurements responsive to a presence of glucose in a fluid; and aprocessor to: detect a change in responsiveness of said glucose sensorto said presence of glucose in a fluid based, at least in part, on anapplication of one or more thresholds to one or more sensitivitymetrics, at least one of said sensitivity metrics being computed based,at least part, on said measurements.
 13. The apparatus of claim 12,wherein said change in responsiveness comprises a decrease insensitivity of said glucose sensor to the presence of glucose in saidfluid.
 14. The apparatus of claim 12, wherein at least one of saidsensitivity metrics comprises a measurement of dispersion of a ratechange in said blood glucose sensor measurements over at least a portionof said time interval.
 15. The apparatus of claim 14, wherein saidmeasurement of dispersion of said rate of change comprises a variance ofsaid rate of change.
 16. The apparatus of claim 12, wherein said changein responsiveness of said glucose sensor to the presence of glucose insaid fluid is detected by: defining a sequence of windows in time; forat least one of said windows, computing a dispersion of said rate ofchange; and determining at least one of said sensitivity metrics based,at least in part, on said computed dispersion.
 17. The apparatus ofclaim 16, wherein said at least one of said sensitivity metrics aredetected by determining said at least one of said sensitivity metricsbased, at least in part, on a ratio of said computed dispersion and amean value of glucose sensor measurements obtained in said at least oneof said windows.
 18. An article comprising: a non-transitory storagemedium having machine-readable instructions stored thereon which areexecutable by a special purpose computing apparatus to: obtain aplurality of glucose sensor measurements at a glucose sensor over a timeinterval; and detect a change in responsiveness of said glucose sensorto a presence of glucose in a fluid based, at least in part, on anapplication of one or more thresholds to one or more sensitivitymetrics, at least one of said sensitivity metrics being based, at leastin part, on said glucose sensor measurements.
 19. The article of claim18, wherein said change in responsiveness comprises a decrease insensitivity of said glucose sensor to the presence of glucose in saidfluid.
 20. The article of claim 18, wherein at least one of saidsensitivity metrics comprises a measurement of dispersion of a ratechange in said blood glucose sensor measurements over at least a portionof said time interval.
 21. The article of claim 20, wherein saidmeasurement of dispersion of said rate of change comprises a variance ofsaid rate of change.
 22. The article of claim 18, wherein said change inresponsiveness of said glucose sensor to the presence of glucose in saidfluid is detected by: defining a sequence of windows in time; for atleast one of said windows, computing a dispersion of said rate ofchange; and determining at least one of said sensitivity metrics based,at least in part, on said computed dispersion.
 23. An apparatuscomprising: means for obtaining a plurality of glucose sensormeasurements at a glucose sensor over a time interval; and means fordetecting a change in responsiveness of said glucose sensor to apresence of glucose in a fluid based, at least in part, on anapplication of one or more thresholds to one or more sensitivitymetrics, at least one of said sensitivity metrics being based, at leastin part, on said glucose sensor measurements.